mohammad-abazari Goto Github PK

garch

GARCH model implementation

granularflowmodels

The software is a list of Abaqus User MATerial subroutines (VUMAT) for modeling granular flow physics. It includes (1) density dependent Mohr-Coulomb model, (2) density dependent Drager-Prager\Cap model, (3)Gudehus-Bauer hypoplastic model, and (4) critical state-based NorSand model.

gtoa

Group Teaching Optimization Algorithm on 10 bar truss

haftka-optimization-2005

EGM 6365-Structural optimization fall 2005 University of florida

jaya-honey-badger-optimization-based-deep-neuro-fuzzy-network-structure-for-detection-of-covid-19-

The Covid-19 virus is fast spreading disease in globally, which threateness billions of human begins. In this paper, Jaya Honey Badger Optimization-based Deep Neuro Fuzzy Network (JHBO-based DNFN) is introduced for Covid-19 prediction by audio signal. Here, Covid-19 prediction is done using DNFN, and it is trained by developed JHBO algorithm. The developed JHBO-based DNFN is outperformed than other existing methods testing accuracy, sensitivity and specificity of 0.9176, 0.9218 and 0.9219. The Covid-19 prediction process is more indispensable to handle the spread and death occurred rate because of Covid-19. However, early and precise prediction of Covid-19 is more difficult, because of different sizes and resolutions of input image. An effective Covid-19 detection technique is introduced based on hybrid optimization driven deep learning model. The Deep Neuro Fuzzy network (DNFN) is used for detecting Covid-19, which classifies the feature vector as Covid-19 or non Covid-19. Moreover, the DNFN is trained by devised Jaya Honey Badger Optimization (JHBO) approach, which is introduced by combining Honey Badger optimization Algorithm (HBA) and Jaya algorithm. The developed JHBO-based DNFN is outperformed than other existing methods testing accuracy, sensitivity and specificity of 0.9176, 0.9218 and 0.9219. Covid-19 is respiratory disease, which is usually produced by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). However, it is more indispensable to detect the positive cases for reducing further spread of virus, and former treatment of affected patients. An effectual Covid-19 detection model using devised Jaya Honey Badger Optimization-based Deep Neuro Fuzzy Network (JHBO-based DNFN) is developed in this paper. Here, the audio signal is considered as input for detecting Covid-19. The gaussian filter is applied to input signal for removing the noises and then feature extraction is performed. The substantial features, like spectral roll-off, spectral bandwidth, Mel frequency cepstral coefficients (MFCC), spectral flatness, zero crossing rate, spectral centroid, mean square energy and spectral contract are extracted for further processing. Finally, DNFN is applied for detecting Covid-19 and the deep leaning model is trained by designed JHBO algorithm. Accordingly, the developed JHBO method is newly designed by incorporating Honey Badger optimization Algorithm (HBA) and Jaya algorithm. The performance of developed Covid-19 detection model is evaluated using three metrics, like testing accuracy, sensitivity and specificity. The developed JHBO-based DNFN is outperformed than other existing methods testing accuracy, sensitivity and specificity of 0.9176, 0.9218 and 0.9219. The recent investigation has started for evaluating the human respiratory sounds, like voice recorded, cough, and breathing from hospital confirmed Covid-19 tools, which differs from healthy persons sound. The cough-based detection of Covid-19 also considered with non-respiratory and respiratory sounds data related with all declared situations. This paper explicates the Covid-19 detection approach using designed Jaya Honey Badger Optimization-based Deep Neuro Fuzzy Network (JHBO-based DNFN) with audio sample. The series of steps followed for introduced Covid-19 diagnosis model are pre-processing, feature extraction, and classification. The input audio sample is acquired from a Coswara dataset and gaussian filter is applied. The gaussian filter effectively reduces the salt and pepper noise with minimal duration. Feature extraction process is most significant for precise detection of Covid-19, where spectral bandwidth, spectral roll off, Spectral flatness, Mel frequency cepstral coefficients (MFCC), spectral centroid, root mean square energy, spectral contract, and zero crossing rate are extracted. The Deep learning approach is effectual for disease detection and classification process in medical field. Here, DNFN is utilized for detecting the Covid-19 disease. Moreover, DNFN is trained by developed JHBO approach for obtaining better performance. The proposed JHBO algorithm is newly devised by combining Jaya algorithm and HBA. Here, Jaya algorithm is incorporated with HBA for obtaining improved performance with better convergence speed. The performance of DNFN is estimated with three performance metrics, namely specificity, testing accuracy and sensitivity. The proposed JHBO-based DNFN achieved improved performance testing accuracy, sensitivity and specificity of 0.9176, 0.9218 and 0.9219.

jburkardt-f77

An official Git Mirror of John Burkardt's great collection of FORTRAN 77 Software

large-scale-truss-optimization

Large scale truss optimization using NSGA-II

matfem

a MATlab Finite Element Method package

math307

Applied Linear Algebra

math307notes

Course notes for MATH 307 Applied Linear Algebra

mathematical-python

Introduction to Mathematical Computing with Python and Jupyter

matlab-sdof-solver

Solve SDOF with any loading function

mohammad-abazari

Config files for my GitHub profile.

mte219-truss-optimization

MTE219-Truss-Optimization

multiaxial-high-cycle-fatigue-life-prediction-model

Multiaxial-high-cycle-fatigue-life-prediction-model

neo-hooke-vumat

Abaqus VUMAT fortran subroutine for a Neo-Hookean material

optimization-benchmark-truss-problems

MATLAB codes for benchmark truss optimization problems

optimization-of-trusses-benchmarks

Practice Problems using Metaheuristic algorithms

optimization_plane_truss-

Optimization of plane truss in matlab

optruss

Truss Optimizer - using Genetic Algorithm

pinn_wind_bearing

Python scripts for wind turbine main bearing fatigue life estimation with physics-informed neural networks

projectmappen

creates projectmappen CFRP

pultrusion

Abaqus user subroutines for numerical simulation of cracking during pultrusion of 80 mm diameter GFRP rod

pymetaheuristic

A python library for: Adaptive Random Search, Ant Lion Optimizer, Arithmetic Optimization Algorithm, Artificial Bee Colony Optimization, Artificial Fish Swarm Algorithm, Bat Algorithm, Biogeography Based Optimization, Cross-Entropy Method, Crow Search Algorithm, Cuckoo Search, Differential Evolution, Dispersive Flies Optimization, Dragonfly Algorithm, Firefly Algorithm, Flow Direction Algorithm, Flower Pollination Algorithm, Genetic Algorithm, Grasshopper Optimization Algorithm, Gravitational Search Algorithm, Grey Wolf Optimizer, Harris Hawks Optimization, Improved Grey Wolf Optimizer, Improved Whale Optimization Algorithm, Jaya, Jellyfish Search Optimizer, Krill Herd Algorithm, Memetic Algorithm, Moth Flame Optimization, Multiverse Optimizer,Pathfinder Algorithm, Particle Swarm Optimization, Random Search, Salp Swarm Algorithm, Simulated Annealing, Sine Cosine Algorithm, Teaching Learning Based Optimization, Whale Optimization Algorithm.

python-yieldcriteria

Various plot functions for yield criteria

python_stable_3d_truss_analysis

slientruss3d : Python for stable truss analysis and optimization tool

remaining-fatigue-life-monte-carlo-simulation

The Monte Carlo Simulationcode for estimating the Remaining Fatigue Life

report-flat-slab-column-connection

sc-yield-function

A C2 continuous yield funciton for basic shear strength, tensile strength cut-off, compressive strength cap, and the impact of intermediate principal stress

sga

Simple Genetic Algorithm - Truss optimization